Human Factors in Highway-Rail Crossing Accidents: The Influence of Driver Decision Style

This paper explores the hypothesis that driver decision-making style influences highway-rail crossing accidents. To investigate this, we have designed an analysis of variance experiment with three independent variables: “driver decision style,” “driver time pressure” and “intersection complexity.” To simulate the driving conditions, we identified and videotaped a number of dangerous crossings in downtown Los Angeles. The tapes represented different environmental complexities and time pressures a driver experiences while crossing an intersection. The tapes were played back to the subject drivers. The subjects were classified according to their decision styles. Dependent measures were designed based on a driver’s decision to cross the intersection. This paper presents the conceptual approach and the experimental design for this research

Investigating the role of driver decision styles in highway-rail crossing accidents.

This research was designed to take a closer look at the ways by which driver decision-making styles affect highway-rail crossing (HRC) accidents. That is, a simplistic approach of portraying human error, as the cause of most HRC accidents, needs to be augmented with a more complex theory of human decision-making process while performing driving tasks before and during a highway-rail intersection. Video and still photos were taken to identify the intersections appropriate for this study. The intersections were among many in the Los Angeles metro area with crossings that demanded certain driver maneuvers with potential accident consequences. Based on these selections, both field and laboratory experimental sessions were designed to study three sets of variables: driver decision styles, conditions in the intersection environment that could influence these decisions (environmental complexity) and the driver maneuvers to cross the intersection. The variable of distraction inside the crossing intersection was also studied using recall versus recognition tests. The parametric tests (analysis of variance) showed significant differences in the drivers' scores for the decision style variable. However, other variables showed no significant results. The same results were shown using the chi-square nonparametric test. These results showed that driver decision style is an important factor in the way drivers perceive and behave in highway-rail crossings. Further research was recommended to study the effect of each intersection design feature on driver behavior.This research was designed to take a closer look at the ways by which driver decision-making styles affect highway-rail crossing (HRC) accidents. That is, a simplistic approach of portraying human error, as the cause of most HRC accidents, needs to be augmented with a more complex theory of human decision-making process while performing driving tasks before and during a highway-rail intersection. Video and still photos were taken to identify the intersections appropriate for this study. The intersections were among many in the Los Angeles metro area with crossings that demanded certain driver maneuvers with potential accident consequences. Based on these selections, both field and laboratory experimental sessions were designed to study three sets of variables: driver decision styles, conditions in the intersection environment that could influence these decisions (environmental complexity) and the driver maneuvers to cross the intersection. The variable of distraction inside the crossing intersection was also studied using recall versus recognition tests. The parametric tests (analysis of variance) showed significant differences in the drivers' scores for the decision style variable. However, other variables showed no significant results. The same results were shown using the chi-square nonparametric test. These results showed that driver decision style is an important factor in the way drivers perceive and behave in highway-rail crossings. Further research was recommended to study the effect of each intersection design feature on driver behavior.Southern California University, National Center for Metropolitan Transportation Research, Los AngelesMode of access: Internet.Author corporate affiliation: Southern California University, Department of Civil and Environmental Engineering, Los AngelesAuthor corporate affiliation: Southern California University, Department of Industrial and Systems Engineering, Los AngelesAuthor corporate affiliation: Southern California University, School of Business Administration, Los AngelesOnline access to video clips associated with the report: http://www.metrans.org/Research/Final%20reports.htm"December 2003."Includes bibliographical referencesResearch. Aug. 2000-July 2001Subject code: PDEIKSubject code: PDEISubject code: CDJISubject code: PDDSubject code: WO

A Systematic Framework for Root-Cause Analysis of the Aliso Canyon Gas Leak Using the AcciMap Methodology

According to the US Energy Information Administration [1], the natural gas industry supports 33% of electricity generation in the US. Despite this critical role, the importance of safety and safety culture in the natural gas industry has not been adequately highlighted. The absence of strict regulations and lack of attention towards precautionary measures have allowed the industry to persevere with insufficient urgency for implementing innovative technologies and safety-first protocols. On October 23, 2015, the Aliso Canyon natural gas accident highlighted how the lack of regulatory oversight in a low probability, high consequence industry could have such impactful and unpredictable repercussions. This paper analyzes the concatenation of events that led to the Aliso Canyon gas leak. It adopts the AcciMap methodology, which was originally introduced by Rasmussen in 1997 as an accident investigation framework, to conduct a systematic root-cause analysis and capture different involved socio-technical factors that contributed to the leak